AI-Based Anomaly Detection of Consumer Device Behaviors

The faster proliferation of smart consumer devices has generated a remarkable volume of complex data, necessitating advanced anomaly detection mechanisms to identify security threats, operational system malfunctions, and inefficiencies. The high dimensionality, heterogeneity, and continually changing conditions can lead to lower performance, mainly when methods are applied across different devices and environments. Furthermore, numerous advanced techniques are based on supervised learning, which necessitates extensive labeled anomaly data-a major limitation given the rarity and labeling complexity of Internet of Things (IoT) anomalies. These constraints underscore essential research gaps in the development of anomaly detection frameworks that are generalizable, scalable, and resilient across diverse IoT environments. This work aims to bridge these gaps by introducing an AI-driven anomaly detection framework, integrating deep autoencoders for feature representation and transfer learning to enhance adaptability. Autoencoders are trained to capture complex and intricate patterns of normal device behavior, identifying anomalies through reconstruction error analysis. A transfer learning strategy is employed to tackle the issue of insufficient labeled anomaly data. This approach enables knowledge from data-rich source domains to be adapted to data-scarce target environments, thereby significantly enhancing the model's generalizability across diverse IoT settings. Experimental results prove the approach's scalability and adaptability, confirming its capacity to provide a tailored, high-performance security solution for smart consumer devices. These findings improve current security and privacy frameworks, enhancing the reliability and trustworthiness of IoT systems.